The individual acoustic and visual tracking networks discussed in Secs. REF  and REF , serve as inputs to the fusion neural network. The architecture of the corresponding DSW estimation networks is depicted in Fig. REF . Its output is subsequently combined with the outputs from both unimodal trackers according to Eq. (REF ).
To further refine the resulting speaker presence probability grid, it is fed into a dedicated refinement network based on the image restoration architecture proposed in [1]}. The parameterization of the model [1]} is adapted to the specific requirements of this work. In particular, the input and output size is set to \(20 \times 24\) , matching the size of the probability grid. Each convolutional layer consists of 128 filters with a kernel size of \(3 \times 3\)  and padding. The refinement operation is valuable to remove outliers and noisy estimates from the output grid, which should only contain peaks in grid cells close to the true speaker positions.
